Permission granted: The

economic value of data assets
under alternative policy regimes
A Lateral Economics report for the Open Data
Institute

March 2016

ii

Executive Summary
Introduction and background

The myriad and continually growing uses of data – public sector information
(PSI) and other data (including research and private data) have great social
and economic value. However, there are formidable challenges in estimating
the economic value of much of this.
•

•

First, the phenomenon of data is vast and multifarious. There are
innumerable data series, all with specific micro-economic
characteristics. Data is typically acquired by intermediaries and/or
developers and distributed via a great many products and services.
Second, it is extremely difficult to arrive at a measure of economic
value (e.g. pound sterling) of the final consumption of data because
so much of it occurs for free. Even if one successfully addresses the
conceptual issues (for instance, by envisaging some consumer
‘willingness to pay’), the practical challenges of obtaining empirical
evidence remain. Free consumption is part of the increasingly
important ‘dark matter’ of GDP.

As much as we might be tempted to think the quantification of such uncertain
effects irresponsible, those who must make important decisions are entitled
to press analysts for their best guess of the indicative magnitudes we could
be dealing with.
As one would expect, given this level of ignorance and uncertainty, existing
empirical estimates of the value of open data vary considerably in scope.
However, they suggest that the value added associated with open data varies
between 0.4 and 1.4 per cent of gross domestic product (GDP) with the wide
margin between these two numbers providing some quantification of our
ignorance.
ODI has asked Lateral Economics to assist it to consider the economic
implications of the commercial terms on which core data sets which form the
bulk of PSI (e.g. land registry data and transport data) are distributed.
Pricing and licencing

Data providers incur various costs in acquiring, curating and distributing data.
They may attempt to recover some or all costs by charging for access to the
data. Or they may go further and maximise their own financial return. At the
opposite end of this spectrum, data can be provided free and open licenced.
In many industries, cost-reflective pricing is efficient. However, with
information goods like data, once it is made publicly available, the marginal
cost of additional distribution is effectively zero. Thus, pricing at above this
point will reduce demand and so curtail some information distribution that
would be cost effective. On the other hand, just as private firms must find the

iii
wherewithal to meet all their costs, so government agencies will sometimes
find it appropriate to charge for data to meet fixed costs even though the
marginal cost of additional distribution is effectively zero. Accordingly, this
study investigates the magnitude of the economic effects of this latter course.
The impact of changing price regimes

To provide an indicative estimate of the impact of shifting from cost-recovery
pricing to open data, we build on prior work that estimates the impact of
reducing the prices of PSI. We estimate that the increase in re-use of data
from removing licence restrictions is similar in magnitude to the impact of
dropping prices to zero. In terms of the value created, a shift from a costrecovery to an open-access regime is likely to more than double the value of
the re-use of the data, adding around 0.5 per cent to GDP.
The impact of moving in the other direction – from an open-access regime to
a cost-recovery regime – will reduce the impact, perhaps by around half, at
least in the shorter term. This is because once the search for new and
innovative uses has been done under open data, those subsequently
charging for data have an interest in preserving that outcome. Nevertheless,
once charging and licensing is introduced, the search for further beneficial
uses for data will be curtailed which will see the loss from charging gradually
climb back towards 0.5 per cent.
The implications of a profit-maximising regime are more uncertain. On the
one hand, the revenue from sale of data is likely to rise – producing a further
fall in demand, suggesting losses greater than 0.5 per cent. However, a
sophisticated profit maximiser would probably do considerably less harm than
might be expected from a firm that priced its data products crudely. It would
seek maximally open options to monetise its data – such as advertising and
freemium access. Further, a savvy profit maximiser might invest in additional
data collection, curation and quality assurance work to optimise the value of
its product. However, our report identifies substantial risks in such a course.
Implications

Across the value chain (with the exception of the acquisition of core data),
there appear to be no material barriers to competition. So we expect
reductions in costs to make their way to the ultimate consumers – the public.
There will be some exceptions (for example, where firms can enhance
existing products) and there will be winners and losers where there are risky
developments. Empirical studies suggest that once open data is provided,
demand for re-use will rise rapidly. However, the full value to final consumers
may take some time to eventuate as new applications are developed.
The two biggest obstacles to further developing the market for core data
assets are, as they have been in the past, apathy and/or opposition within
data providers to opening their data and investing in optimising its quality for
users rather than solely the PSI producers. But the UK is a world leader in
tackling these issues. And with further effort comes further opportunity.

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Table of contents
EXECUTIVE SUMMARY

II

TABLE OF CONTENTS

IV

1 INTRODUCTION

1

2 BACKGROUND
2.1 An overview of open data assets
2.2 The value chain for open data
2.3 Estimating the value of open data
2.3.1 Empirical studies

1 Introduction
The mission of the Open Data Institute (ODI) is to ‘connect, equip and
inspire people around the world to innovate with data’. A cornerstone of
achieving this mission is encouragement of open access to data,
particularly that produced by government agencies. The ODI seeks to
better understand the implications of charging for data and has
engaged Lateral Economics to explore the economic implications of
paid access compared to open licencing.
Lateral Economics has been asked these questions:
1) What is the expected economic value to a national economy from
core data assets under two different access models?
a) Paid access – where all organisations must sign a contract,
pay a fee and potentially abide by licence restrictions on the
purposes that the data can be used for
b) Open licence – where anyone can access, use and share the
data
2) Assess how this value will accrue across the different parts of the
value chain that use the data to deliver products/services to the
market.
3) What is driving any difference in economic value between the two
access models, e.g. network effects, time saving, allocative
efficiency, etc.?
4) What are the expected timescales to realise the change in
economic value if data is moved between these different access
models (for example, from paid to open, or from open to paid)?
5) What steps can be taken to accelerate the change in economic
value?
The rest of this document addresses these questions:
•
•
•
•
•

Chapter 2 provides background including a description of the
value chain and recent estimates of the value of open data
Chapter 3 explores the economics of paid and unpaid access
in a qualitative way
Chapter 4 offers an indicative quantification of those issues
Chapter 5 offers comments on the remaining matters in our
terms of reference
Chapters 6 and 7 incorporate References and Appendices

2

2 Background
2.1 An overview of core data assets
The core data assets that are the subject of this study include
data assets such as land registries, ordnance surveys,
meteorological data and transport movements. This data
includes data that is purposefully collected (e.g. national
statistics and meteorological data) and data collected as a byproduct or some other function (for instance, business
registration and court records). These data sets are most
commonly referred to as public sector information1 (PSI) as they
are almost always generated by (or for) public agencies, referred
to in this report as PSI holders (PSIH).
Discussions on the re-use of data, commonly focus on PSI. However,
there are other sources of data. Two other important sources are:
•

research or science data, especially that arising from publicly
funded research, and

There are a great many applications of such data. Coupled with
growth in the growth of applications, there has been growing
recognition that such data has great value.
The data can be used to add value in myriad areas of the
economy in myriad ways. It:
•

reduces costs in providing services both by government and
private sector (i.e. doing the same for less cost)

•

enables new services and improved quality of services, and

•

improves accountability for government services indirectly
improving responsiveness and performance and in the
process engendering greater trust in government.

Many of the benefits accrue directly to consumers of products
and services that have made use of the data. However, there are

1

Note, in this report, except where otherwise made clear, the words “data” and
“information” are used interchangeably.

3
also benefits that accrue to the wider community. These benefits
include, for example:2
•

public benefits associated with improved transparency of
government

•

improved social cohesion, and

•

positive externalities that may arise. For example, one
person’s use of transport data to optimise transport usage
can improve traffic management and reduce congestion for
other users.

2.2 The value chain for re-use of data
There are many descriptions of the value chain for the re-use of
core data assets.3 For our purpose, we propose to describe the
value chain in terms of the following groups:
1. Data providers – these include PSIH, other government
organisations and private organisations and individuals
2. Intermediaries, including
a. data aggregators who source data from existing
open data sources into a more useable form
b. data enablers who facilitate the supply or use of
data through reorganisation and reformat
3. Providers of products and services to consumers
including
a. developers who create applications for individual
consumption
b. data users who use data to enhance existing
products and services or create new ones
4. End users (being the ultimate beneficiaries), including:
a. direct users of the end product, and
b. others who indirectly benefit from the open data
usage by direct users.
The connection between the groups is depicted in Figure 1
below.

The market characteristics of each group are discussed in
Section 3.3.5. However, we note here that publishers or core
data-sets are typically sole suppliers of information because they
have privileged (often statutory) access to information, there are
sunk costs in collection, they receive government funding or
because they have an established reputation for quality. As a
result, data providers have (at least some) power in determining
data prices.
Reviewing the pricing policy for PSI, a European Commission
study (De Vries et al., 2011, pp. 25-30) finds it useful to

4

Any of the stages of production may be conjoint with any other. Someone using
Google Maps will often be providing data back to the app, for instance, on
traffic flow. Data providers may deal with intermediaries or product and service
providers and release data to consumers. Further, there may be multiple chains
before reaching the end user. Thus, government data providers may share data
with other agencies. Similarly, there may be multiple steps in the value chain
before a product or service is provided to an end user. A 2006 survey of
businesses regarding their use of PSI, found that less than 30 per cent of
businesses used PSI to make products for consumers (OFT, 2006, pp. 28-29).

5
categorise (direct) end users as being high end or low end. It
describes the high-end market as consisting of a relatively small
number of re-users that provide high value-added services to
meet the needs of professional clients.5 In contrast, the low-end
market consists of re-users providing low value-added services
to a large number of clients (e.g. mashing up free content to
integrate into other services). Such a distinction may be
important for pricing policies, as the high-end market will be less
price sensitive.

2.3 Estimating the value of open data
There is a literature of reasonable size – to which Lateral
Economics has contributed – estimating the benefits of open
data (defined as data that anyone can access, use and share).
This study seeks to measure the contribution of open data to
gross domestic product, or GDP (which is measured by summing
gross value added, GVA, through the production chain). Though
the limitations of GDP are well recognised,6 it is a convenient
common metric.
Further, measuring the impact of some policy requires
comparing different reference points (or scenarios). For example,
the ‘current’ value of open data might be considered as the value
that would be lost should the relevant data not be available.
However, this later, hypothetical, scenario is difficult to envisage.
It leads to consideration of what substitutes the market might
develop should existing data be inaccessible.
The period over which value is created is also relevant. We are
interested in the future, but this may differ substantially from the
past (which we can measure). Other measurement challenges
include:
•

there are a great many end uses of the products;

•

there is very little data on the end use of the products; and

•

the prices paid by consumers – very often zero – will
generally be significantly less than the value derived.

5

De Vries et al. (2011, p. 25) give the example of a meteorological company that
provides very detailed weather forecasts to oil rigs, based on enhancing PSI
data.

6

More broadly, government policy should generally be concerned with wellbeing
for citizens in the present and the future.

6
Thus we risk significantly, perhaps hugely, underestimating the
value of data if we derive values from observed prices.
2.3.1

Empirical studies

Economic evaluations of the impact of open data may focus on
particular applications or, as in this study, the whole economy.
Similar economy-wide evaluations have varied in scope. Notable
dimensions include:
•

The sources of data considered. Most studies have focussed
only on PSI. Other studies also consider the value of
research data and private sector data.

•

The region considered. Most studies have been limited to a
single country (e.g. the UK) or a select group of countries
(e.g. the G20).

•

Sectors considered. Some studies have focussed on a
specific sector (e.g. transport).

•

The scope of benefits considered, in particular, whether
wider benefits (such as relating to reduced corruption) are
included.

•

Whether the value considered is an existing value or some
measure of potential value. Some studies have just focussed
on the net benefits; that is the value added less the cost of
production.

In Table 1 below, we summarise estimates derived from the
results of some key studies. To facilitate comparison, for some
studies we have applied some additional analysis to the results
presented in the study (see footnotes to the table) and for
consistency and convenience we express these as a percentage
of GDP.
The table is divided into two sections. The top section describes
studies that have attempted to measure the current contribution
of open data. The bottom section refers to two recent studies
that attempt to estimate the potential additional contribution of
open data.

7
The approaches used vary. The approach used by PIRA (2000)
has been described as “top-down” as it begins with value added
and then attempts to assess the contribution of data to the value.
OFT (2006) contrasted this with a bottom-up approach which is
based on attempting to estimate the value derived (interpreted in
terms of willingness-to-pay) by consumers.7
We expect the values of open data (as a percentage of GDP) to
increase over time due to the rapid expansion of new
applications and the greater opportunities for re-use by
consumers as a result of increased penetration of digital devices.
The narrowest scope scenario we might consider is the current
net benefits to direct users. The widest scope is future gross
value added from PSI and other data to direct users and the
wider economy. For the purposes of evaluating the issue of paid
access for core data sets, we propose the appropriate reference
point is core data assets , broad in terms of beneficiaries, and
incorporating the value added in the near term (i.e. more than is
just presently realised).
Based on the existing studies, we concluded that the current
GVA of core data assets to the economy is in the order of 0.4-1.4
per cent of GDP.

7

OFT (2006, p. 114) raised the concern that the top-down approach risks
overestimation in part because it takes no account of the possible use of
alternative data sources that might be used. Conversely, a risk of the bottom-up
approach is underestimation, not least because of difficulties in accounting for
wider impacts.

Source: Lateral Economics analysis. See Table 2 in the appendix for further
details. A summary of many of the studies is provided at Lateral Economics
(2014, sections 3.2 and 3.3).

8

Extrapolated from results and parameters of median estimate reported in
Pollock (2011). The author had estimated the welfare gain of moving to free
and open-access. We applied the core assumption and parameter values to
estimate the value of open data to GDP.

9

Vickery’s method was based on extrapolating estimates from previous studies.

10

We are not clear as to how this estimate was derived.

11

See Lateral Economics (2014, p. 30). This is the estimated contribution to
cumulative GDP growth over the next five years.

12

See Lateral Economics (2014, section 3.5).

9

3 Implications of a paid-access regime
This chapter examines the contrasting economic implications of paid
and open access to data. It begins with a description of the alternative
regimes. To assess the economic impact of a paid-access regime
relative to an open-data regime, one must first identify and describe the
implications of paid access relative to open data. Subsequent sections
explore specific aspects of the economics of data.

3.1 Charging regimes for data
This review compares paid- and open-access regimes. It is useful to
distinguish between multiple models including:13
•

Paid access
o profit maximisation — whereby the data provider sets
prices to maximise its profit
o cost-recovery of data production — pricing to recover the
costs of data production
o cost-recovery of initially establishing data distribution for
re-use
o marginal cost pricing of additional distribution — setting a
price equal to the cost of supplying data to an extra user,
which for digital data is essentially zero, and

•

Zero-priced access — where data is not charged for but subject to
restrictions on its use and redistribution.

•

Open data — data that anyone can access, use and share.14

Generally, we would expect the prices charged and the revenues
raised to be lower as we move down the list above.15
13

This list is adapted from Pollock (2008, p. 8). Pollock also notes that many
PSIHs also have the ability to charge those parties providing updates to the
information. For example, PSIHs dealing with registration of property, vehicle
and company ownership may fund their data collection and processing
activities from those registering the item.

14

See http://theodi.org/blog/closed-shared-open-data-whats-in-a-name.

15

A risk with subsidised supply is that there will be an excessive supply (e.g.
investment in the provision of data). Conversely, there is the risk that there will
be insufficient incentive for investment. However, there will be exceptions,
particularly where demand is very responsive to price. De Vries et al. (2011)
noted cases where the lowering of prices resulted in increased revenues.
Furthermore, as discussed below, a profit maximising data provider might
employ a mix of pricing strategies. Another argument for cost recovery is that

10

3.2 Implications of different access regimes
To assess the implications of different access regimes, we first
consider the common rationale for paid access before considering
issues particular to open data.
3.2.1

The common rationale for paid access

In most markets, users pay, and this is highly efficient. User charges
ensure that resources needed to provide services are taken up only
where their value is demonstrated by users’ willingness to pay. In this
way, data consumers decide whether or not to fund the collection and
dissemination of that data. In terms of the charging schemes discussed
above, the argument often leads to an incremental cost charging
regime, whereby the supplier (in this instance, the data provider)
recovers the incremental cost of providing access.16 A pricing regime
can, therefore, send a signal as to the optimal investment.17
3.2.2

‘Abundance thinking’: The economics of information
goods

However, while the fixed costs of data provision is often large, marginal
costs of additional dissemination are often negligible. If prices are set
above marginal cost, then additional use will be discouraged even
though its benefits exceed the (negligible) additional costs. As in many
other industries, marginal cost pricing leads to an under-recovery of
costs as there is no revenue to fund fixed costs. In essential services,
such as energy and water, the problem may be addressed with two
sets of charges corresponding to fixed and marginal costs. However,
such a strategy is only effective when the fixed price does not deter
access.18 Outside government, very low marginal costs of information

the alternative suggests raising additional taxation revenue. This is costly
because taxes generally distort behaviour away from what is socially optimal.
16

As is discussed in Section 3.2.2.1, profit-maximising pricing can also be
efficient under the (albeit seemingly rare) conditions that the supplier is able to
differentiate its charging so as to capture any consumer surplus.

17

With regard to open data, this is likely to be the greater risk. As noted by
Pollock (2008, p. 13), requiring an organisation to charge at less than average
cost can reduce the incentive for the organisation to develop new products.

18

Two-part pricing is also operative on the internet where internet service
providers typically charge a fixed fee for access to the internet and nothing
further for use (sometimes up to some cap on data usage).

11
provision has produced various business models. In most services for
the mass market, resources are provided by means other than prices.19
3.2.2.1

Profit maximisation and price discrimination

A concern with paid access is that the data publisher will attempt to go
beyond the recovery of incremental costs and instead maximise its
profit20 Prima facie this would increase prices and further reduce
demand. However, there are additional considerations. Profit
maximisation may stimulate demand by price discriminating; charging
users different prices depending on their willingness to pay.
In principle, perfect price discrimination is as efficient as perfectly
competitive pricing, but the informational and behavioural demands on
the seller to bring this about are Herculean.21 In practice, price
discrimination is usually difficult and can result in additional waste.
Suppliers of information goods adopt a number of strategies. These
include:
•

Discriminating using quality of product or service whereby a lower
price is offered for lower quality products. Common strategies are:
o Windowing – whereby the product is brought to market at
different times in different formats. For example, films are
released first in the cinemas and then at a later date onto
other mediums.
o Versioning – whereby the product is released with differing
levels of quality.
o Service modifications – whereby there are differences in
the level of support.

•

Bundling, whereby to obtain a product, consumers are required to
purchase a bundled package.

19

Open-source software leverages the voluntary efforts of software users fixing
bugs or adding features. Wikipedia uses philanthropy to run a platform that
users volunteer their time on. Facebook, Twitter and Google provide their
services for free while ‘monetising’ the social value they create from advertising
revenue. Elsewhere a dominant strategy is ‘freemium’ – a form of price
discrimination we explore further in the following section.

20

By this term, we do not mean marginal costs, but rather the full cost arising
from the public distribution of the data. This may involve the full costs of all data
collection, curation and distribution, or where governments already collect
and/or curate the data in any event, all additional costs in curating and hosting
the data for public release.

21

Thus each buyer’s willingness to pay must be known and charged without
anyone arbitraging the differences in prices in the downstream market.

12
Where lower quality products generate inconvenience or lower utility to
the consumer, whilst saving the producer nothing, economic losses
arise.22
Freemium
There is a growing army of products funded through ‘freemium’
business models in which data and other services are freely distributed
whilst those seeking higher levels of quality or service pay. Free
services are effective marketing including lowering buyers’ transactions
costs by allowing them to ‘try before they buy’. Freemium products
include LinkedIn, Google Apps, Evernote, Dropbox, Feedly, Pocket.
Chris Anderson (2009) coins the term ‘abundance thinking’ to describe
the mindset that produces ‘freemium’ pricing – making the abundance
of the digital world and its zero marginal cost of production – work for
consumer and user alike. As Gruen (2015, p. 5) puts it, while funding
the fixed costs of these services raises the free rider problem, the nearzero marginal costs of expansion mean that the free rider opportunity
will often trump the free rider problem.
Given all this, it is likely that a sophisticated profit maximising data
provider would do considerably less harm than might be expected if a
firm were to price its data products crudely. It is even possible to
imagine circumstances where profit maximisation would provide
incentives to invest in additional data collection, curation and quality
assurance work, and that this could increase economic welfare above
the level that might be achieved by a relatively mediocre government
agency administering government mandated policies of open data.23

22

Since the price discriminating firm’s sole interest is to maximise its access to
consumer surplus without regard to the resulting disutility to customers, it may
do more harm to the total utility to consumers than its expansion of supply
benefits economy-wide welfare. These issues are not new. The economist
Jules Dupuit raised concerns in 1849 with regard to price discrimination of
railway carriages with little left undone to make conditions unpleasant for third
class passengers, not to save costs, but to avoid second class passengers
buying third class tickets. Today’s mobile phone packages may well provide a
contemporary example of price discrimination which lowers general wellbeing
given the additional costs of staying within plans and the cost of informing
oneself of their respective terms. Nevertheless, the digital age gives us a new
twist as the complexity of modern mobile phone plans also establishes a
‘confusopoly’ making it harder for consumers to understand various trade-offs.

23

Google Maps has invested substantial sums in generating and curating data for
distribution which it can monetise by charging premium users. Nevertheless, it
offers free access to a standard product for the vast bulk of direct users of the
service. https://developers.google.com/maps/pricing-and-plans/#details

13
Nevertheless, it seems unlikely, and there are further risks in such a
course. First, the entrepreneurial flair of a highly innovative profit
maximising incumbent might give way to more complacent behaviour in
the future in which a more mature firm’s managers use their monopoly
position to meet quarterly revenue and profit growth targets. Second,
the data would almost certainly be distributed according to licencing
restrictions which is likely to seriously curtail economic welfare (see
Section 3.2.3 below). Third, if we can imagine excellence in
harmonising general economic welfare with profit maximisation, we can
surely imagine excellence in the public sector which can target
economic welfare more directly without the additional imperative of
profit maximisation with all the distortions it entails here.
3.2.3

Transactions costs

The transactions costs borne by consumers and the suppliers differ
greatly between access regimes. The process of vending data is almost
by definition more complex than simply disseminating it for free. The
costs to the supplier include administrative costs such as invoicing, as
well as costs of managing a licensing and compliance regime, and De
Vries concluded they were significant (2011, p. 6). They include:
•
•
•
•

Building an online sales environment where the qualities of
data are described pending their sale.
Building the relevant security layers or sub-contracted platform
services to take commercial payment.
Commissioning the work to know what kind of licensing terms
to impose and then the legal work to design those terms.
Considering whether or not to take action against those who
breach them and, if so, funding that.

Nevertheless, there are potentially more profound forces at work.
Information is a non-rival good. Use in one application does not
preclude use in another. And with near zero costs of distribution, even
small transactions costs can be a big deal as has been recently
illustrated on the internet (See Box 1 below).

14
Box 1: The significance of transactions costs on the net
The global phone network and the internet are both built around
‘interconnect’ agreements in which nodes on the network
exchange access to each others’ users. The phone network
facilitates dedicated connections between users. So large telcos’
negotiate interconnect agreements fiercely, with each seeking to
maximise its ‘cut’ of the economic rent.
The internet works by routing addressed data packets, each
making its own opportunistic way through the net depending on
network conditions. If someone won’t negotiate interconnect
reasonably, others can be found and, so, few are tempted to
negotiate unreasonably. As a result, transactions costs between
service providers negotiating reciprocal access to each others’
services collapse. Virtually all - 99.5 per cent – of reciprocal
access agreements occur informally without written contracts.
What does this mean for efficiency and productivity? On an
equivalent voice-per-minute rate, internet rates are around one
hundred thousandth of typical voice rates.
The collapse of transactions costs in cyberspace has led to the
burgeoning of new social and economic formations. Anyone –
including (crucially) any innovator – can access the network
without requiring the permission of, or paying rent to, monopolistic
gatekeepers – as one must with telephone or TV networks.
Adapted from Gruen (2012).

Prices impose transactions costs on users and, given that they are
borne by each user, they constitute a potentially much larger source of
deadweight loss. These costs not only include administrative costs
such as those associated with reviewing licence agreements and
making financial transactions but also — as described by Szabo (1999)
— “mental transaction costs” to consumers. Szabo has categorised
them into costs associated with dealing with uncertain cash flows,
observing product attributes and complexity of decision making. If these
are ‘cognitive’ costs, there are also ‘psychological’ transactions costs.
As Chris Anderson has documented (2009), free is a very special price
and, for many consumers, a quantum leap beneath very low prices.
Free means free of the risk of losing money, free of being taken
advantage of, free to trust or to suspect vendors after inspecting their
goods.
The implications of transaction costs are demonstrated in the figure
below, which documents the huge rise in the availability of book titles
on the market once copyright expires. The paradox is that there is
demand for books from which publishers and copyright owners could
make some profit (as the sale of out of copyright books demonstrates),

15
which they nevertheless forego. In other words, in the absence of
transactions costs, one would expect more book titles to be in print
during the copyright term rather than less, because the copyright
increases the potential profit in their sale. And the magnitude of the
effect is large – with book titles reduced by over 80 per cent.
Figure 2: Book titles in print from Amazon warehouse by decade

Source: Rosen, 2012.

There are also other costs to consumers to consider. A significant risk
to commercial users of open data is that future supply will no longer be
available or its quality will fall or that access will become more limited.
Such concerns are in effect a cost borne by consumers. In sum, the
transactions costs associated with charging for data and/or licensing
that data to control redistribution can be substantial, but largely
disappear under an open-data regime.
3.2.4

Network externalities and innovations

For many information goods, and in particular for data assets, both the
supply and consumption of data can stimulate greater demand for
several reasons. First, there can be consumption externalities. Thus,
for instance, people using real-time transport data to avoid congestion
lower congestion for everyone. Second, the rate of adoption of a
particular service may increase with the penetration of the market due
to the social influence of early adopters on later adopters.24

24

For example, persons who use a data-enabled app (e.g. that provides real-time
transport) may do so because they were told about it by a friend and/or
influenced in their decision to adopt it from observing others. Such effects are
commonly discussed in the literature on ‘diffusion of innovations’.

16
Third, there are network effects associated with different re-use. Great
value can often be derived when data sets are integrated with one
another; for instance, TripAdvisor adds to the accumulated value of
geospatial data and customer ratings data to help people identify and
find travel destinations to their taste. Greater value still could be
generated if its maps also integrated with live transport data. The
greater the number of data-sets accessible, the lower the technical and
commercial barriers to their integration, the greater the value generated
by each data series.
Finally, there are supplier network effects. Increasing the number of
developers using a particular data-set can stimulate additional
development through a number of mechanisms. Greater re-use can
result in economies of scale in the provision of intermediary services
(i.e. by aggregators and enablers). Perhaps more significantly, there
are network benefits in terms of innovation as developers help each
other out in developer communities. Prima facie, we might expect that a
charge on information would not materially inhibit the development of
an innovation where the benefits far exceed the information costs.
However, empirical evidence suggests that even a small charge may
significantly impede innovation.25 There are several reasons for this.26
First, the returns to the innovation may be highly dispersed among
suppliers. The parties purchasing the data may not expect to recoup
their investment as most of the value is captured further down the value
chain. Second, the cost of obtaining information (including the costs
associated with licensing) may need to be borne by multiple parties
involved in development. Third, sellers are unlikely to know of all the
ways their data can be valuable to others, and the magnitude of that
value and this uncertainty is likely to make negotiating access a fraught
process as each party seeks to capture what it sees as its share of
benefit.
The combination of the above effects have prompted a number of
parties to argue that the priority strategy for information goods should
be on abundance of use as this will in turn stimulate greater supply and
demand.27

25

Pollock (2008, Appendix A2) notes that “Weiss (2004) argued, marginal cost
access to weather data in the US was a large factor in the development of the
multi-billion dollar weather derivatives industry”.

26

Some of these are discussed by Pollock (2008, Appendix A2).

27

There appears to be broad support from researchers (e.g. Pollock 2011,
Vickery 2011, and Shakespeare 2013) for open data. There is also public
support. De Vries et al. (2011, pp. 10-12) note that the majority of responses
from public consultation were in favour of free access.

17

4 Estimating the impact of a paidaccess approach
4.1 Overview and approach
The discussion above highlights why, for several reasons, paid access
results in sub-optimal re-use of data. To estimate the impact of paidaccess, we have used the estimates from Chapter 2 on the value of
open data as a baseline. In effect, we are estimating the economic
value lost through paid access.
The impact of paid access relative to open data depends in part on
which paid-access pricing approach is employed; whether, for instance,
it focuses on profit maximisation rather than cost recovery. The next
section considers methodological issues.

4.2 Basic approach
The difference between open access and paid access to data is
illustrated in Figure 3 below. It shows the demand for core data assets
and the ‘effective price’ paid under different pricing regimes. We have
defined the ‘effective price’ as the financial cost plus the costs
associated with complying with any licence agreements. Of note, this
price under a free-but-restricted regime is greater than zero.28 A shift
from open data to ‘free but restricted’ will increase transactions costs
for sellers (see above) and may also raise ‘mental transactions costs’
for consumers. It is also likely to depress indirect demand for the data
by those who might have otherwise received the data through the initial
customer, but did not because the customer was not authorised to pass
it on.29 The indicative shape of the demand curve in Figure 3 is
consistent with the conclusions of De Vries et al. (2011, pp. 25-30) who
argue that sufficient price reductions open up a large low-end market.
The supply curve (which reflects the marginal cost of supply) varies by
regime. Under open access it is, in effect, the horizontal axis.30 Another
related effect not captured in the illustration is that under paid-access

28

There are potentially alternative ways of illustrating this additional impact, (e.g.
including a separate demand curve) however, this seemed the simplest
approach.

29

Thus, for instance, if Hansard data was only available directly to users, but not
for free redistribution, the organisation They Work For You,
(http://www.theyworkforyou.com/) which substantially increases the distribution
of Hansard information, might have been discouraged from distributing it.

30

More precisely the marginal cost of supply is high for the first consumer but
effectively zero for each subsequent user.

18
regimes, the cost of supply also shifts upwards (from zero to a positive
amount).

A shift between charging regimes will be associated with a change in
the value added and societal welfare. However, there are several
problems with attempting to quantify these effects. Estimating the
demand is particularly difficult. There is some information collected on
demand and how this responds to changes in price;31 however, this will
not be representative of the welfare associated with open data.
As Pollock (2008) notes, there are two key issues. First, data is
typically distributed to intermediaries and developers, not end
consumers. The demand information captured, therefore, does not
represent what the final consumers are willing to pay and the welfare
gains to consumers. Because data can be re-used at negligible cost
and developers are not able to capture many of the resulting consumer
benefits, they are likely to underestimate them by a considerable
amount. With much of the data supplied at zero price, there is no
market signal of its value.32
Second, the information captured will represent the demand when it
was captured, yet with the rapid change that characterises the area, the
present may be a poor guide to the future. 33
31

Pollock (2008) provides a useful summary.

32

For example, simple economic accounting for the value of Google would
suggest that it is limited to its value as an advertiser, yet more sophisticated
attempts to measure its economic value produce conservative estimates
several times higher than this with debate ranging from ten to one hundred
times the amount directly recorded in GDP. See Worstall (2015).

33

There are numerous additional issues in measurement. For example, the
volume of direct access to a data-set may decrease as a result of consumers

19
Another set of issues relates to assessing the pricing regimes that
might emerge. As we discussed in Section 3.2.2.1, an organisation
might employ a variety of pricing strategies and business models,
including approaches that simultaneously seek to maximise profit and
re-use.

4.3 Modelling the effect of changing price
regimes
In this section, we estimate the effect of a shift between paid and openaccess pricing regimes. Our initial focus is on the change between cost
recovery and open data. We then consider the implications of a profitmaximising pricing regime in which price-discrimination policies might
be applied.
4.3.1.1

A model for estimating the impact of paid-access

A useful starting point is the work of Pollock (2008 & 2011).
Considering a number of the limitations itemised above, Pollock
developed a model to estimate the welfare effects of moving from
average cost to marginal cost pricing for PSI as a function of.
• the fixed costs incurred in producing and maintaining the PSI
• the responsiveness of direct consumers (technically the price
elasticity of direct demand), and
• a demand multiplier that reflects the difference between direct
customers’ willingness to pay and the total value provided to all final
customers, many of whom have no direct relationship with the data
provider.
Of course, the challenge with this approach is obtaining reasonable
estimates for the key parameters. The fixed costs of providing data may
be estimated with a reasonable degree of certainty; however, direct
observation of the elasticity of demand and the demand multiplier is not
possible. Pollock offers estimates of elasticity and the demand
multiplier based on a review of evidence from several sources.
Pollock’s model (summarised in Box 2 on page 31 in the appendix) is
reasonably intuitive. The more elastic (price responsive) the demand,
and/or the greater the multiplier, the greater the loss from charging for
data.

choosing to access the information via new applications developed by
intermediaries. For example, all else being equal, the volume of direct users of
meteorological data sets may fall as a result of the development of weather
apps on smart-phones that access the data via intermediaries.

20
Using his model, Pollock (2011) estimated the welfare gains in the UK
in 2011 from ‘opening up' (i.e. moving to marginal cost pricing). His
estimates ranged from 0.11 per cent to 0.4 per cent of GDP, around
four to 11 times the cost of providing PSI.
A study on Danish address data (DECA 2010) provides one opportunity
to test the estimate. The study estimated that the annual benefits of
open address data were EUR 14 million at an annual ongoing cost of
EUR 0.2 million. Other information in the report suggests the cost of
providing the data was higher. Nevertheless, the case study provides a
result that is above the higher range estimated by Pollock.34
4.3.1.2

Refining the model

Pollock estimated the change to a marginal cost pricing regime, under
which he notes it would be “natural for the PSIH to make the data
‘openly’ available” (2008, p. 9). However, our interpretation is that the
model and parameter estimates are more consistent with a reduction in
pricing and not a removal of restrictions on use. In particular, Pollock
assumed that the demand curve is linear (i.e. does not curve as
illustrated in Figure 3) and uses evidence of elasticity estimates that
included cases where prices were reduced but were not made free.
As discussed in section 4.2, we expect that transaction costs for uses
of data are significant and that, as a result, demand will expand
significantly when moving from a free-but-restricted regime to an opendata regime. To account for this, we extend Pollock’s model to include
a kink when transaction costs are removed (see appendix 1 for details).
This approach brings new challenges. As discussed below, there is
some anecdotal evidence on the increase in direct demand when
shifting an open-access regime is introduced.
However, we must place a value on that additional demand. There are
a number of considerations. In standard economic models, all
economic agents have perfect knowledge and are perfect competitors
and this means that lower priced uses are lower value uses. We take it
as a reasonable assumption of the more complicated reality.
Generally the assumption will be reasonable, but it will impart a
downward bias on estimates of the value of open data. The increased
search facilitated by negligible transactions costs will probably facilitate
the serendipitous discovery of some unanticipated high-value uses.
34

Other information in the DECA (2010) report indicates that the cost of
distributing PSI had been higher. The paper reports the costs of the agreement
over 5 years to move to open data were EUR 2 million (i.e. EUR 0.4 million per
year). Using this latter figure gives a benefit to cost-of-provision ratio of 35 to 1.
However, this is unlikely to be indicative of the average result as high value
opportunities are more likely to be enacted, studied and reported.

21
And the value of both existing and new uses will probably be magnified
by the strengthened network externalities associated with burgeoning
re-use.
We also expect that the average value added lost by transaction costs
will be related to the size of these costs. That is, the greater the
transaction costs, the greater the average value added that is lost. To
aid calculation, we assume that the value added per new re-use under
open data is in direct proportion to the size of the transaction costs that
are removed in moving to open data.
Using a model described in the appendix, we can estimate the change
in GVA as follows
?? + ???
GVA under open-access regime
= 1+
GVA under cost-recovery regime
2 + 1/??
Where:
?=

?!
?!

?!
?!
?!
?! =
?!
?! =

the ratio of transaction costs to the monetary costs
paid by direct users under incremental cost
recovery
the increase in demand from a cost-recovery
regime to a free-but-restricted regime
the additional increase in demand from a free-butrestricted regime to an open-access regime

Following’s Pollock (2011)’s work, we use an estimate of ?! = 2 and,
therefore, the above equation can be simplified to:
GVA under open-access regime
= 1.8 + 0.4??!
GVA under cost-recovery regime
Based on other case studies (see section 7.3), we think it reasonable to
suggest that ?! is around 2 to 4 (with a midpoint of 3).
For the parameter t, we have found no existing estimates. Based on
our experience on similar issues and our own experience in acquiring
data, we think it conservative to suggest that these transactions costs
are around one-third of the financial costs of a purchase.35 In such
cases, using the above formula, we have a GVA under open data of
around 2.2 times the GVA under a cost-recovery regime.

35

In considering this issue, we considered the time taken to review agreements
and the ‘mental transaction costs’ of adhering to the agreements. In our
experience these costs increase with the financial value of the contract and
therefore the (average) value of the t parameter may not vary significantly with
higher-cost data sets.

22
If we were to use recent estimates of GVA under a cost-recovery
regime of around 0.4 per cent of GDP, then the GVA under an openaccess regime would be in the order of 0.9 per cent; that is, an
additional 0.5 per cent of GDP.
4.3.1.3

Shifting between other pricing regimes

The above analysis considered the implications of changing from cost
recovery to open data. The impact would probably be less moving from
open data to cost-recovery pricing because many benefits of open data
arise from the way it facilitates the search for new data applications.
Once established, many will likely remain.36
It is also of interest to consider what might occur when shifting between
profit maximisation and open data. As discussed above, profit
maximisation may involve more complex pricing strategy – for instance,
differentiated pricing such as ‘freemium’ to encourage re-use amongst
lower value users.
Ultimately, the impact of profit maximisation depends on the strategy
adopted by the organisation. At one extreme, an organisation
introduces a simple charging mechanism that aims to maximise the
short-term revenue from the data. At the other extreme, an organisation
adopts a strategy that attempts to optimise profits over the longer term
and/or across a broader business base.37 A third possibility is
something in between, whereby attempts to implement differentiated
pricing result in waste.
Clearly, the change in GVA between these two extremes is large.

36

Note, however, as the example of Amazon book titles in print suggests, that
those with an interest can still leave ‘money on the table’ where transactions
costs offset its value sufficiently. Nevertheless, once data has found its way into
useful applications, makeshifts will often be found to maintain these
arrangements generally by way of renegotiations of access to the data.

37

For example, Google Maps offers differentiated pricing regimes which
encourage re-use by small users and attempts to recover costs from greater
abundance of use.

23

5 Further matters
5.1 Implications across the value chain
How will the value of free and open data accrue through the data value
chain introduced in sub-section 2.2?
5.1.1.1

Data publishers

Data publishers could use their market power to maximise profits,
increasing profits in the short term. However, we doubt this would be
substantial for several reasons. First, the public good nature of digitised
data makes it difficult for any publisher to capture much of the
consumer surplus generated. To prevent downstream competition
between direct customers receiving their data, a data publisher seeks
to control distribution, removing competition in downstream markets;
however, this would likely lead to problems. If the data provider is not
vertically integrated with the data developer, there is a risk of double
marginalisation whereby both monopolists attempt to maximise profit
and, in combination, reduce the value they obtain.38 The data provider
may attempt to solve the problem by vertical integration; however, this
is likely to be relatively inefficient as it results in the data provider
undertaking services outside its core capability.
Second, often some form of substitute can be generated. For much
public data, there are potentially other (though sometimes more
expensive or less efficacious) ways of obtaining substitute data. Thus,
for instance, if it is no longer possible to obtain data from traffic
authorities on the speed of traffic, or if it has risen in price, one can
seek it from mobile phone carriers who can measure the speed of
mobile phone movement on the road. Third, there are substantial costs
associated with employing charging mechanisms to counter the issues
above. The combination of these factors suggests that charging may
significantly increase costs whilst reducing demand.
5.1.1.2

There are several companies that offer data aggregation services. An informal
review of some companies is available at http://www.eveahearn.com/judgingopen-data-aggregators/ (accessed 22/1/2016).

24
to be competitive. There appear to be few material barriers to entry into
the market, though we expect substantial investment is required in
systems and marketing and, therefore, the primary market will be
contested by a discrete number of larger firms with smaller
organisations competing in niches.
Given this, we expect a shift to charging would see aggregators
negotiating with publishers over pricing in the short term with
aggregators’ profits falling somewhat. Over time, the market will adjust
(e.g. with some aggregators exiting or new entry falling) such that the
average profitability of aggregators remains relatively stable.
Enablers

Core data assets are often in a format that developers find difficult to
work with. So-called ‘enablers’ address this problem by further
processing the data, for instance, by providing an application program
interface (API), a set of routines, protocols, and tools for building
software applications.40 The market for enablers appears similar to that
of aggregators, with substantial fixed costs but no barriers to entry and
plenty of room for competition and for self-provision amongst its
customers.
5.1.1.3

Product and service providers

Broadly, there are two types of product and service providers:
•

Developers who create applications for individual consumption

•

Data users who use data to enhance existing offerings

In the ‘developer’ market, there are no material barriers to entry.
However, the success of any product may be highly uncertain. The
‘data user’ category consists of providers of established products. In
these markets, the suppliers of products may have some market power.
The enhancement to the established products may result in a greater
return to those established providers. The ‘data user’ beneficiaries will
typically include other government organisations,41 who would, we
expect, pass on the value to the public through improved services or
reduced costs.
For example, the increased re-use of core data assets has increased
the value to final consumers from owning smart mobile devices to the
benefit (greater producer surplus) of those suppliers of such devices.
Nevertheless, competition (or the threat of competition) will typically
limit the extent to which such providers will be able to capture the
40

An example of an enabler is http://www.transportapi.com/.

41

For example, DECA (2010, pp. 2, 5) concluded that around 30 per cent of the
benefits from open access to Danish address data accrued to the public sector.

25
value. Furthermore, the greater re-use of data will reduce profits of
suppliers of products that are displaced by the data re-use. A simple
example is that of providers of maps whose business has been
transformed by digitisation of data.
5.1.1.4

Consumers

As indicated in this sub-section 5.1.1, competition through the value
chain will deliver most of the additional value created from open access
to end data consumers.

5.2 Timing
5.2.1

Timing of effects

We outline below a number of what seem reasonable scenarios
regarding the timing of market effects. The time period over which the
demand impact modelled in section 4.3 should be regarded as
reasonably short (in the order of one or two years), suggesting that the
full effects of shifting from paid access to open data are felt reasonably
quickly.
However, there are some other considerations. The studies referred to
changes in direct demand which will include intermediaries and
developers. There will be a lag — which may be quite significant —
from the time that developers acquire the data to the time that value is
realised in the form of products and services and widely adopted in
society .
The speed of change will also depend on the direction of change. The
studies examined looked at the impact of price reductions; none
examined the impact of price increases or the introduction of more
onerous pricing regimes. The first-round impact of price rises is likely to
be faster as the search for value adding uses of the data has already
been done. Here the market will move fairly quickly to new price
configurations, with some further adjustment as buyers and sellers test
each other out and react to counter-party responses.
5.2.2

Accelerating the change

How can we accelerate the change in the economic value when
transitioning from paid access to open data? Change may be slow for
various reasons. De Vries et al.’s case studies (2011) highlight the
importance of removing barriers to reform including reliance on data
revenues, organisational constraints and perceived risks to change.
They note that public sector bodies relying on PSI sales revenues and
value adding appear deadlocked “when there is no other sustainable
alternative income stream available”.

26
They also noted, “Further barriers to change relate to statutory
provisions imposing cost-recovery schemes, the legacy of old re-use
regimes, and the sheer difficulty of changing existing practices”, and
noted incumbent re-users with considerable interest in preservation of
status quo may try to prevent PSBs lowering charges.
They noted that change could be driven by a top-down process (e.g. by
political mandate) or by a bottom-up process (i.e. from within the
organisation). In the case of the latter, additional effort was required to
justify the reform and secure funding for the transition. Regardless, the
study noted “the PSBs interviewed declared that a clear path to
transition and the financial means to do so have been of crucial
importance”.
And with the market changing fast, measures to deepen market
development will also help accelerate the achievement of beneficial results
as set out in the following sub-section.

5.2.3

Market development

5.2.3.1

Fostering additional investment in data curation

Some data, such as meteorological and geographical data, is created
for its use to those downstream, and so in this sense, is created
essentially for its value to users (even if it is rarely created by those
users). However, other data is often a by-product of other activities –
for instance, registration and tax data. In these cases, data may be
published without much regard to its usefulness. As a result, those
creating and curating the data will have little incentive, and often little
knowledge of what uses the data may be best put to, or how further
investment in the curation and documentation of the data may add
value to downstream users. This data curation will be a public good to
those downstream who may use and add value to the data.
Accordingly, they should have some role in the governance of data
curation and dissemination.
Mechanisms might be developed to allow downstream users to identify
and build on opportunities. For example, by PSIHs facilitating feedback
mechanisms and performing tasks (at cost) for those prepared to
curate and prepare additional data.42 Additional incentives might be
provided by enabling PSIHs to obtain additional funding for further data
curation.43
42

Subject to any privacy, security or other technical concerns, they allow
outsiders into their systems to work on the data themselves.

43

For example, by providing subsidies to PSIHs to assist with some of the costs
of further data curation by outsiders on the grounds that the resulting benefits
cannot be captured entirely by those doing the work. Other opportunities might
involve granting PSIH’s time-limited monopoly privileges over the improved

27
5.2.3.2

Building value

The great data projects driven by the private sector have tended to
accumulate around digital artefacts – generally platforms – that
generate value that draws in users. Those users then contribute their
data.44 While government agencies do not generally, and should not,
seek a competitive advantage over anyone, they should seek to
generate value where they can. And those in government in an
incumbent position frequently pay too little attention to serving users
and generating value as an integral part of their operating strategy.
Thus, in addition to making data available, government agencies could
give some thought to fostering value creation with that data.
In addition to further investments in curation (discussed in the previous
sub-section) governments can seed the development of communities of
practice – rather like the community around an open source software
project – with an increasing user base generating positive network
externalities for all members of the growing community to enjoy. In
addition, they may be able to seed projects and/or the development of
platforms which might grow into ‘data traps’. While this may not sit
easily within departments of state with fundamental line responsibilities,
certainly more independent agencies tasked with market development
like the ODI might seek to pursue such goals possibly in collaboration
with other private and public interests. And such initiatives might also
sit well with innovation units within line agencies.
It should be noted that such an approach swings the government into
the business of using its own assets to seed deeper data markets –
built not just on PSI and government resources in establishing a
platform, but also on private data.45
Governments can do this using their own resources to seed platforms,
they can help those platforms succeed by nudging or compelling their
own agencies to contribute.46

data and/or given PSIH’s capacity to levy stakeholders (e.g. In Australia
farmers have statutory powers to collectively levy themselves in Australia to
fund public good research).
44

As Matt Turck (http://mattturck.com/2016/01/04/the-power-of-data-networkeffects/) has put it, “An approach I particularly like is building a ‘data trap’. The
idea is to build something that delivers real, tangible value to users from the
beginning, and incite them to start contributing their data’.

45

An example is discussed in Gruen (2015), “Innovation? How about TripAdvisor
for the arts?” 27th Dec, 2015 in The Age, http://goo.gl/iTjwOj.

46

For example Lateral Economics report commissioned by Omidyar Network, by
contributing to the development of standards which draw out others’ data
because the standard has now enhanced its informational value.

We modify the Pollock model by introducing a kink to the demand curve
and explicitly consider the impact of transaction costs. This is illustrated
in Figure 4 below, which (similar to the Pollock model) shows the direct
demand for data assets. To reflect transaction costs, the figure
presents demand in terms of the perceived price, which includes
transaction costs.
The figure shows a two-part demand curve to better approximate the
real demand. While the first part of the demand curve is identical to

47

The ‘2/5’ amount in the equation reflects the assumption that the demand curve
is linear and an adjustment for distributional consequences of the subsidy
which reduces the welfare loss by a factor of 4/5. Pollock (2008) argues that
benefits from lowering the price of PSI are received in proportion to income
and, therefore (from a welfare perspective), there is an adverse distributional
impact of subsidising PSI. We are sceptical of the need to apply this
adjustment. However, in our opinion, an adjustment of similar magnitude is
appropriate to account for the marginal excess burden of taxation.

48

These estimates are from Pollock’s 2011 paper. These parameter estimates
(both for of ? and ?) are higher than suggested in Pollock’s earlier (2008) work
as the scope of the PSI considered was broader.

32
Pollock’s linear demand curve, the second part is kinked with elasticity
rising reflecting the fact that, once the data is open licenced, the
distribution of the data becomes ‘permissionless’, powerfully reducing
frictions which would otherwise frustrate the data finding its way to
valuable uses.

Empirical evidence suggests that the second part of the demand curve
is much flatter than the traditional demand curve (which will be
discussed in the next subsection). In other words, the transactions
costs associated with the chain of permissions to distribute data in a
licenced regime deter a sizeable amount of users from utilising data
assets. Using this basic approach, we can estimate the proportion of
value loss from a paid-access regime using a few parameters:49
•

the proportionate increase in demand that occurs when moving
between pricing regimes, and

•

the significance of the transactions costs in proportion to the costs
of acquitting the public sector data.

49

Note: the diagram illustrates a situation in which new users brought into the
regime from its move to open licensing will gain relatively low value as they are
further down the demand curve. This assumption tends to underestimate the
value of increasing demand from permissionless distribution. This is because
the transactions costs of licensing frustrate search for users and once search
costs fall and new uses are found, it seems likely that some new uses will turn
out to have relatively high value. For instance, some of the users introduced to
automated voice directions while driving on Google Map’s free system would
experience a functionality from the service which, had they known of it before,
would have induced them to pay for positively priced services like Navman or
Tomtom. This possibility is discounted in our treatment.

33
In the diagram:
•

?! is the per-user monetary cost of information under cost recovery
pricing

•

?! is the per-user transaction cost in a free-but-restricted pricing
regime

•

?! is the quantity demanded under cost recovery pricing and ?!
and ?! is the additional quantity under free-but-restricted regime
and open data.

The additional welfare of moving from cost recovery to marginal cost
(free-but-restricted) is the area D times a demand multiplier (λ). If the
demand multiplier (λ) is constant across the demand curve, then the
GVA of core data asets under cost recovery is λ (E+F) and under a
free-but-restricted regime is similarly λ(E+F+D).
In moving to an open-access regime, two additional effects happen.
There is an increased demand reflected in the area C. There is also a
reduction in transaction costs to existing re-users equal to the area
A+B. To determine the increased GVA associated with increased reuse, we have taken a similar approach by multiplying the area C by the
same demand multiplier; that is, λC.
The reduction in transaction costs for existing users (area A+B) would
have a net-welfare benefit but would not impact on GVA. Similarly,
there would be a reduction in transaction costs for suppliers of data
assets. Similarly, this reduction would have a net-welfare benefit but no
impact on GVA.
The impact of moving from cost recovery to open access is, therefore,
to increase GVA from (E+F)λ by the amount (D+C)λ.
!!!

As a multiple, the increase in GVA is 1 + !!!

We set up three ratios to help solve the model.
?!
the ratio of transaction costs to monetary costs
?=
?!
?!
the increase in demand from a cost-recovery
?! =
?!
regime to a free-but-restricted regime
?!
the increase in demand from a free-but-restricted
?! =
?!
regime to an open-access regime
The areas C, D, E and F can all be computed as a function of F. These
are:
•

? = ½??! ?! = ½??! ?

•

? = ½?! ?! = ½?! ?

34
•

? = ½?! ?! = ½?/?!

Therefore, shifting from a cost reflective to an open-licence regime will
increase GVA by a multiple of:
1+

?! + ??!
D+C
=1+
?+?
2 + 1/?!

Consistent with Pollock (2011)’s mid-range estimate of elasticity, we
assume ?! to be equal to 2 (Of note, his higher estimate is 3.5). As
discussed in the next sub-section, we assume that ?! is between 2 and
4 (with a mid-point of 3).

7.3 Evidence of changes in response to price
A number of studies have examined the changes in demand for PSI as
a result of changes in prices. Pollock (2008) provides a survey of
evidence of price elasticity estimates for PSI. The elasticity analysis in
Pollock is complemented by a more recent study — De Vries et al.
(2011) — that involved 21 in-depth case studies where public sector
bodies (PSB) had changed prices. The case studies were divided into
four domains, where the three major domains each encompassed a
100 per cent price-cut case.
A brief summary of the cases reported in these two papers is provided
below. While the elasticity estimates in Pollock’s papers are within “a
large range”, the sensitivity of quantity demanded can be alternatively
inferred from De Vries et al.’s case studies. Using these results, we
developed estimates of the parameters for the modelling. For change in
demand from cost-recovery to marginal-cost pricing, we have assumed
an increase of 200 per cent. This is consistent with Pollock’s (2011)
mid-range estimate. To estimate the impact of open data, we more
closely examined the changes in demand reported by De Vries et al.
(2011). As can be seen from the summaries, there are very large
demand increases following price cuts.
When comparing cases, there have been 100 per cent price cuts. With
cases with slightly small price cuts, we observe very different changes
in the usage increase — with a 100 per cent price cut, the increase is
much more significant. In terms of the monetary costs to customers, the
100 per cent price cut cases are similar with (for example) a case
where there is a 97 per cent price cut case. Because the latter case is
not completely free of charge, there will be transaction costs, which we
expect to be the main driver for the usage difference.
In both the Meteorological and the Geographic domain examples
provided (from De Vries et al. 2011) below, the increase in demand
following a 100 per cent price cut was around three times as great.
That is, for example, if shifting to close-to-zero prices leads to a 200 per

35
cent increase, then shifting to free and open access would result in an
additional 400 per cent increase (for a total of 200x3 = 600 per cent).
The additional increase we observe could also in part be attributed to
further price reductions (from near to zero to zero). In conclusion, we
think it is reasonable to assume that moving from free-but-restricted to
open-access will (in terms of demand) lead to an additional 200 to 400
per cent increase in the demand for core data assets (i.e. suggesting
?! will be between 2 and 4).
In light of the above analysis, we have assumed for a mid-range
estimate ?! = 2 and ?! = 3.
De Vries et al. (2011)

In this study, four domains, Meteorological PSI, Business register PSI,
Geographic PSI and Other PSI were examined. A summary of demand
change in response to price changes are as follows.
Meteorological PSI:
•

KNMI — following an 80 per cent price cut, the number of re-users
increased by 1,000 per cent.

•

Met.no — following a 100 per cent price cut, the number of reusers grew by 3,000 per cent.

Geographic PSI:
•

BEV — following an up-to-97 per cent price cut, usage volume
increased, which includes: 250 per cent increase for digital
cadastral maps, 200-1,500 per cent increase for cartographic
products, 7,000 per cent for digital orthophotos, 250 per cent for
the digital elevation model, 1,000 per cent for the digital landscape
model, and 100 per cent increase in external-use licenses.

•

Spanish Cadastre — following a 100 per cent price cut, the number
of digital maps downloads increased by 800 per cent, alphanumeric
data downloads increased by 1,900 per cent, total downloads
increased by 965 per cent.

Other PSI:
•

Destatis — following a 100 per cent price cut, the number of unique
visitors increased by 1,800 per cent; and the number of downloads
increased by 800 per cent.

From Pollock (2008)

Pollock (2008) documented a number of cases about the sensitivity of
demand when there is a price change (see Pollock 2008, for the
references).

36
•

The Office of Fair Trading (2006) — estimated an elasticity of 0.3
(lower bound) and 2.2 (upper bound) for New Zealand national
mapping data.

•

Davies and Slivinski (2005) — estimated an elasticity of 0.3 for
demand of weather forecasts. This was considered as a lower
bound because it excludes demand coming from intermediaries
and the private sector.

Making Information Freely Available initiative, Statistics New
Zealand — estimated elasticities from lowering prices of 6 for
Digital Boundaries Files, 34 for Street Link Files, 1.5 for Small Area
Population Estimates.

•

The Australian Bureau of Statistics — estimated an elasticity of
2.33 (short-run) and 3.5 (long-run) for ABS statistics.